Digital panacea or not: Platform Participation
–A critical approach to quantify mass qualitative urban experience from social media platform
Yuanzhao WANG
Supervisor: Carole Voulgaris
Abstract
Understanding and reflecting urban dwellers’ happiness, and perception of urban space and life in urban planning has long been a challenge in China. The Chinese government has progressively brought attention to public participation and views, which had previously been ignored. However, government approaches for fostering public engagement remain scarce, while enthusiasm among individuals to express feelings and comments on government planning is lacking. Over 70% of the Chinese population has access to the internet, and 68% of them utilize social media, which can serve as a platform for gathering public sentiment and feelings. In this context, my research project proposes measuring individual citizens’ emotions and characteristics of the built environment, the proximity to various urban amenities, to explore the relationships between citizens’ sentiments, urban space, and urban activities. This approach can be applied to gather public feelings in the urban planning process through platforms, as a way to quantify the quality of urban life. However, research reveals serious flaws in existing social media data that could merely provide satisfied information for urban planning. The research examines the strengths and weaknesses of social media platforms and proposes a vision for using social media to invite bottom-up citizen participation in urban planning.
Introduction
In China, the early stage of urbanization was subject to the national ambition and development, whose aim at boosting the economic production and consumption. As such, hundreds and thousands of cities and towns were built to serve for this purpose that plays an important role in improving the national economy based on the scarification of natural resource and incline of factory production. However, since the decreasing demand for industrial production and the arise of urban awareness of residents happiness, Chinese government increasingly focus on citizen wellbeing, urban governance and the urban environment.
The new urban policies in China shifted the emphasis of urbanization away from economic development and toward human-centric development, improving residents’ wellbeing and building new ecological smart cities. However, the evaluation of residents’ wellbeing and urban planning process remains top-down and entirely conducted by governments and experts. The qualitative surveys and reports could only cover a small proportion of population, and it becomes even harder and time-consuming as the population in cities and towns reached 848 millions.
With the introduction of social media data and machine learning technologies, new methods for studying urban spatial patterns and residents’ life have emerged. In 2022, there were more than 1.02 billion people have access to mobile internet, while 68% of them frequently use social media platform. The AI-based technology such as sentiment analysis could be used to extract individual feelings from text-based information, as a form of public perception of urban space, contributing to bottom-up engagement and people-oriented urban planning.
In comparison to the traditional top-down approaches to urban research, emerging technologies and smart city concepts becomes a temptation for both government and academia. However, prior to blindly applying urban big data, a thorough validation is required even it seems to be promising. The author proposes a method for gathering public sentiment via social media, investigates its relationship to the built environment, and examine its potential to urban planning strategies. The discussion of the strengths and drawbacks of existing social media data leads to a proposal for an improved application of social platforms for public participation.
Literature Review
Wellbeing as a Measure of Health and the Built environment
Scientists have historically measured well-being using objective indicators (e.g., GDP, health, employment, literacy, poverty) and increasing measured subjective well-being that influences individual life. Modern measures of well-being that account for cognitive evaluations (i.e., evaluative well-being) and reactions to experiences (i.e., experienced well-being) have therefore become the “currency of a life that matters” (Rath et al., 2010). As the concept of well-being develops, the indices including physical health, mental health, air quality and more are increasingly used, implying a strong relationship between health and residents’ well-being (Diener et al., 1999; Lawless & Lucas, 2010). Some studies found that population density may affect well-being on the city level (Florida et al., 2013). Mouratidis (2018) argues that compact urban form with better public transport, accessibility, the mix of land uses, and density positively influences neighborhood well-being. Social and human capital, considered significant drivers of urban well-being, can be affected by safety, educational opportunities, and access to arts and culture (Leyden et al., 2011; Florida et al., 2013). Other aspects of urban infrastructure (such as roads and transportation) impact commute time and connectedness, both of which are related to happiness (Yin et al., 2021; Gim, 2021).
Quantitative urban measure of the built environment
The measurement of the built environment is constructed by a variety body of indices to address different urban issues. Cervero and Kockelman’s developed initial “three Ds” (density, design, and diversity) in 1997 to evaluate the existing urban built environment. Edwing et al. expanded on this concept by adding two Ds (destination accessibility and transportation distance) (Ewing & Cervero, 2001; Ewing et al., 2009). More Ds were added afterwards to reflect the changing built environment, such as Demand management and Demographics (Ewing & Cervero, 2010). Scholars have modified the list of variables based on these quantitative frameworks to comprehensively examine the built environment while addressing various urban issues and topics. Some research used relative entropy to discern compactness from sprawl in the built environment (Tsai, 2005). Others used a multi-metric urban intensity index at a metropolitan scale, which included land use, infrastructure, and landscape variables in addition to density and compactness (Tate et al., 2005). More recent studies, especially in the Chinese context, Rowe et al. (2014) proposed the measurement of urban intensity from variables of compactness, density, diversity, and connectivity, aiming at revealing the resource distribution, transportation efficiency, and social integration in both cities and optimize the urban performance (Rowe et al., 2014). Later, Guan and Rowe (2016) evaluated the spatial structure of small towns in Zhejiang Province using similar urban intensity characteristics, such as density, compactness, diversity, and accessibility.
Limitation
- First, although the relationships between public sentiment and the proximity to amenities are indicated by a set of regression models, there are no proof to suggest any causation;
- Second, in this research, author assumed the public sentiment from social media could represent individual real-time happiness, which remains uncertain awaiting a further examination;
- Third, due to the limited urban variables, the confidence level of regression model is relatively low at 2%, which could be used to study the changes of individual sentiment but is incapable to simulate a convincing results;
- Forth, the inherent conflict between short-term resident’s feeling from existing social media data and long-term urban planning process is recognized;
- Fifth, the existing social media data is not specific for the built environment or urban experience but daily expression of everyday life;
- Sixth, the weibo post could only represent the users who mostly age from 16-40, which are around 30% of the total population who have access to internet.